This Rmarkdown file provides a simple tutorial on how to use the functions in SpatialQPFs R library to calculate the features to decipher spatial relationship between different cell types, from spatial statistics and graph topology perspective.
Firstly, let’s simulate a synthetic dataset to begin with. In reality, to extract detailed cell-level information, users must preprocess the digital image. This preprocessing step involves isolating key information such as cell coordinates and cell identity from a variety of digital slides, which can encompass images stained with H\(\&\)E, Immunohistochemistry, or Immunofluorescence techniques, as well as spatially resolved single-cell imaging.
library('SpatialQPFs')
library('data.table')
library('dplyr')
set.seed(42)
# library('ggplot2')
#
# # --- 1. Set Parameters ---
#
# # Set a seed for reproducibility
# set.seed(42)
#
# # Define the number of cells for each region
# n_main <- 2000 # Cells in the general tumor microenvironment
# n_hotspot <- 300 # Cells in the lymphocyte-infiltrated hotspot
# n_stroma <- 500 # Cells in the stroma-dense region
#
# # Define the four cell types
# cell_types <- c("TC", "Lym", "Stroma", "Others")
#
#
# # --- 2. Generate Data for Each Spatial Region ---
#
# # Region 1: General Tumor Microenvironment (TME)
# # High proportion of TC, moderate Stroma, low Lym and Others.
# dat_main <- data.frame(
# x = round(runif(n = n_main, min = 1, max = 10000)),
# y = round(runif(n = n_main, min = 1, max = 10000)),
# cell_id = sample(
# cell_types,
# size = n_main,
# replace = TRUE,
# prob = c(0.50, 0.10, 0.30, 0.10) # 50% TC, 10% Lym, 30% Stroma, 10% Others
# )
# )
#
# # Region 2: Lymphocyte-rich Hotspot
# # Simulates a strong immune infiltrate with a very high proportion of Lymphocytes.
# dat_hotspot <- data.frame(
# x = round(runif(n = n_hotspot, min = 1500, max = 4000)),
# y = round(runif(n = n_hotspot, min = 1500, max = 4000)),
# cell_id = sample(
# cell_types,
# size = n_hotspot,
# replace = TRUE,
# prob = c(0.10, 0.70, 0.15, 0.05) # 10% TC, 70% Lym, 15% Stroma, 5% Others
# )
# )
#
# # Region 3: Stroma-dense Area
# # Simulates surrounding connective tissue with a high proportion of Stroma.
# dat_stroma <- data.frame(
# x = round(runif(n = n_stroma, min = 6000, max = 9500)),
# y = round(runif(n = n_stroma, min = 5000, max = 8500)),
# cell_id = sample(
# cell_types,
# size = n_stroma,
# replace = TRUE,
# prob = c(0.15, 0.05, 0.75, 0.05) # 15% TC, 5% Lym, 75% Stroma, 5% Others
# )
# )
#
#
# # --- 3. Combine and Finalize Data ---
#
# # Combine the data frames from all regions
# dat <- rbind(dat_main, dat_hotspot, dat_stroma)
#
# # Shuffle the rows to ensure the data isn't ordered by region
# dat <- dat[sample(nrow(dat)), ]
#
# colnames(dat) <- c("x", "y", "cell_id")
#
#
# # --- 4. Visualize the Generated Data ---
#
# # Create a scatter plot to visualize the spatial distribution of cell types
# cell_plot <- ggplot(dat, aes(x = x, y = y, color = cell_id)) +
# geom_point(alpha = 0.8, size = 1.5) +
# coord_fixed() + # Ensure the aspect ratio is 1:1
# scale_color_manual(values = c(
# "TC" = "firebrick",
# "Lym" = "steelblue",
# "Stroma" = "forestgreen",
# "Others" = "grey50"
# )) +
# labs(
# title = "Spatially Simulated Cell Data",
# x = "X Coordinate",
# y = "Y Coordinate",
# color = "Cell Type"
# ) +
# theme_minimal() +
# theme(
# legend.position = "bottom",
# plot.title = element_text(hjust = 0.5, face = "bold", size = 16)
# )
#
# # Print the plot
# print(cell_plot)
#
# # Display the first few rows of the final data frame
# print(head(dat))
#
# write.csv(dat, '/Users/lix233/SpatialQPFs_2.0.0/inst/extdata/dat.csv', row.names = F)
dat = read.csv(system.file("extdata", "dat.csv", package = "SpatialQPFs"))
cat("The first 20 cell in the example csv file looks like:")## The first 20 cell in the example csv file looks like:
## x y cell_id
## 1 3779 8206 TC
## 2 2655 470 TC
## 3 1088 5104 Stroma
## 4 7313 4994 Stroma
## 5 8735 7233 TC
## 6 9450 9375 Others
## 7 597 5852 Stroma
## 8 2679 4492 TC
## 9 1786 2610 Lym
## 10 9792 8173 TC
## 11 1297 1465 Lym
## 12 4142 6521 TC
## 13 2908 1577 Stroma
## 14 1601 2656 Lym
## 15 7149 4428 TC
## 16 1888 2316 Others
## 17 9471 5543 TC
## 18 8537 7951 Stroma
## 19 1252 9848 Others
## 20 9819 7061 TC
Now we have the generated the data, we want to visualize the cell
data, this can be done by calling Data_Vis() function.
We look at “Lym” cell type first:
path = gsub("dat.csv", "", system.file("extdata", "dat.csv", package = "SpatialQPFs"))
# print(paste0("Your file path is: ", path))
file = "dat.csv"
Data_Vis(path = path, file = file, cell_class_var = "cell_id", x_var = "x", y_var = "y", cell_type = "Lym") Next, “TC” cell type:
Data_Vis(path = path, file = file, cell_class_var = "cell_id", x_var = "x", y_var = "y", cell_type = "TC") Next, “Stroma” cell type:
Data_Vis(path = path, file = file, cell_class_var = "cell_id", x_var = "x", y_var = "y", cell_type = "Stroma") Next, “Others” cell type:
Data_Vis(path = path, file = file, cell_class_var = "cell_id", x_var = "x", y_var = "y", cell_type = "Others") To utilize spatial point process methods, SpatialQPFs provides 3 functions:
Point_pattern_data_uni()Point_pattern_data_bi() and
Point_pattern_data_ITLR(). For details of the functions,
refer to help(Point_pattern_data_uni),
help(Point_pattern_data_bi), and
help(Point_pattern_data_ITLR)For “Lym” cell type spatial features:
ss_point_uni_Lym = Point_pattern_data_uni(path = path, file = file, x_var = "x", y_var = "y", cell_class_var = "cell_id", cell_type = "Lym", scale = 500, myplot = T)## $g_AUC
## [1] 0.004661152
##
## $g_r
## [1] 0.05066369
##
## $k_AUC
## [1] 0.0003789604
##
## $k_vals_med
## 50%
## 0.00789986
##
## $k_vals_q1
## 25%
## 0.00198158
##
## $k_vals_q3
## 75%
## 0.01722362
##
## $k_vals_max
## [1] 0.02926199
##
## $pcf_AUC
## [1] 0.1330209
##
## $pcf_vals_med
## 50%
## 3.776434
##
## $pcf_vals_q1
## 25%
## 3.49762
##
## $pcf_vals_q3
## 75%
## 3.913852
##
## $pcf_vals_max
## [1] 5.176213
##
## $pcf_vals_min
## [1] 3.383061
##
## $pcf_r
## [1] 0.00266651
##
## $CE
## naive
## 0.890238
For “TC” cell type spatial features:
ss_point_uni_TC = Point_pattern_data_uni(path = path, file = file, x_var = "x", y_var = "y", cell_class_var = "cell_id", cell_type = "TC", scale = 500, myplot = T)## $g_AUC
## [1] -3.399809e-05
##
## $g_r
## [1] 0.04474579
##
## $k_AUC
## [1] 7.373894e-06
##
## $k_vals_med
## 50%
## 0.002108313
##
## $k_vals_q1
## 25%
## 0.0005112325
##
## $k_vals_q3
## 75%
## 0.004734315
##
## $k_vals_max
## [1] 0.008146708
##
## $pcf_AUC
## [1] 0.002099142
##
## $pcf_vals_med
## 50%
## 1.067271
##
## $pcf_vals_q1
## 25%
## 0.9994965
##
## $pcf_vals_q3
## 75%
## 1.08681
##
## $pcf_vals_max
## [1] 1.106596
##
## $pcf_vals_min
## [1] 0.7911587
##
## $pcf_r
## [1] 0.01052842
##
## $CE
## naive
## 1.008257
For “Stroma” cell type spatial features:
ss_point_uni_Lym = Point_pattern_data_uni(path = path, file = file, x_var = "x", y_var = "y", cell_class_var = "cell_id", cell_type = "Stroma", scale = 500, myplot = T)## $g_AUC
## [1] 0.00104726
##
## $g_r
## [1] 0.05001
##
## $k_AUC
## [1] 0.0001153919
##
## $k_vals_med
## 50%
## 0.003684848
##
## $k_vals_q1
## 25%
## 0.0009141326
##
## $k_vals_q3
## 75%
## 0.008340983
##
## $k_vals_max
## [1] 0.01473958
##
## $pcf_AUC
## [1] 0.04155797
##
## $pcf_vals_med
## 50%
## 1.87003
##
## $pcf_vals_q1
## 25%
## 1.84434
##
## $pcf_vals_q3
## 75%
## 1.902831
##
## $pcf_vals_max
## [1] 1.979815
##
## $pcf_vals_min
## [1] 1.781335
##
## $pcf_r
## [1] 0.002632105
##
## $CE
## naive
## 0.9585885
For “Others” cell type spatial features:
ss_point_uni_Lym = Point_pattern_data_uni(path = path, file = file, x_var = "x", y_var = "y", cell_class_var = "cell_id", cell_type = "Others", scale = 500, myplot = T)## $g_AUC
## [1] -0.002346612
##
## $g_r
## [1] 0.05021089
##
## $k_AUC
## [1] -1.958382e-05
##
## $k_vals_med
## 50%
## 0.001847102
##
## $k_vals_q1
## 25%
## 0.0003459852
##
## $k_vals_q3
## 75%
## 0.003609473
##
## $k_vals_max
## [1] 0.007333462
##
## $pcf_AUC
## [1] -0.005840654
##
## $pcf_vals_med
## 50%
## 0.8691311
##
## $pcf_vals_q1
## 25%
## 0.7951248
##
## $pcf_vals_q3
## 75%
## 0.9635727
##
## $pcf_vals_max
## [1] 1.104238
##
## $pcf_vals_min
## [1] 0.6671293
##
## $pcf_r
## [1] 0.04492553
##
## $CE
## naive
## 1.130428
For spatial interaction features between “TC” and “Lym”:
ss_point_bi = Point_pattern_data_bi(path = path,
file = file, x_var = "x", y_var = "y", cell_class_var = "cell_id",
from_type = "Lym",
to_type = "TC",
scale = 500, myplot = T)## Cross type G-function is calculated.
## Cross type K-function is calculated.
## 1,
## 2.
## Differences in Ripley's K-function is calculated.
## Pair correlation function is calculated.
## Mark correlation function is calculated.
## Mark connection function is calculated.
## Marcon and Puech's M function is calculated.
## $g_cross_AUC
## [1] 0.0006782376
##
## $g_cross_r
## [1] 0.03889667
##
## $k_cross_AUC
## [1] 2.426612e-05
##
## $k_cross_vals_med
## 50%
## 0.002336459
##
## $k_cross_vals_q1
## 25%
## 0.0005660821
##
## $k_cross_vals_q3
## 75%
## 0.005277495
##
## $k_cross_vals_max
## [1] 0.00944047
##
## $K12_Diff_AUC
## [1] 0.0003790916
##
## $k_k1k2_vals_med
## 50%
## 0.00594408
##
## $k_k1k2_vals_q1
## 25%
## 0.001634323
##
## $k_k1k2_vals_q3
## 75%
## 0.01270813
##
## $k_k1k2_vals_max
## [1] 0.02151166
##
## $K1K12_Diff_AUC
## [1] 0.0003633184
##
## $k_k1k12_vals_med
## 50%
## 0.005750551
##
## $k_k1k12_vals_q1
## 25%
## 0.001602771
##
## $k_k1k12_vals_q3
## 75%
## 0.0122064
##
## $k_k1k12_vals_max
## [1] 0.02026278
##
## $K2K12_Diff_AUC
## [1] -1.577328e-05
##
## $k_k2k12_vals_med
## 50%
## -0.0001935285
##
## $k_k2k12_vals_q1
## 25%
## -0.0005017328
##
## $k_k2k12_vals_q3
## 75%
## -3.155209e-05
##
## $k_k2k12_vals_max
## [1] 0
##
## $pcf_AUC
## [1] 0.008457301
##
## $pcf_vals_med
## 50%
## 1.21454
##
## $pcf_vals_q1
## 25%
## 1.168805
##
## $pcf_vals_q3
## 75%
## 1.22244
##
## $pcf_vals_max
## [1] 1.229431
##
## $pcf_vals_min
## [1] 1.091761
##
## $pcf_r
## [1] 0.04445334
##
## $mrcf_AUC
## [1] 0.01127915
##
## $mrcf_vals_med
## 50%
## 1.228653
##
## $mrcf_vals_q1
## 25%
## 1.20611
##
## $mrcf_vals_q3
## 75%
## 1.274725
##
## $mrcf_vals_max
## [1] 1.277776
##
## $mrcf_vals_min
## [1] 0.8980466
##
## $mrcf_r
## [1] 0.02778333
##
## $mccf_AUC
## [1] -0.0003683821
##
## $mccf_vals_med
## 50%
## 0.05376596
##
## $mccf_vals_q1
## 25%
## 0.05269271
##
## $mccf_vals_q3
## 75%
## 0.05565426
##
## $mccf_vals_max
## [1] 0.05800203
##
## $mccf_vals_min
## [1] 0.04952488
##
## $mccf_r
## [1] 0.04445334
##
## $M_AUC
## [1] 0.1063055
##
## $M_vals_med
## 50%
## 3.591923
##
## $M_vals_q1
## 25%
## 3.280865
##
## $M_vals_q3
## 75%
## 4.116922
##
## $M_vals_max
## [1] 4.611528
##
## $M_vals_min
## [1] 0.4818792
##
## $M_r
## [1] 0.03334
ss_point_ITLR = Point_pattern_data_ITLR(path = path,
file = file, x_var = "x", y_var = "y", cell_class_var = "cell_id",
from_type = "Lym",
to_type = "TC",
micron_per_pixel = 0.5,
myplot = T)## Xrange is 1 4998
## Yrange is 1 4974
## Doing quartic kernel
## $ITLR
## [1] 0.1852679
##
## $ITLR2
## [1] 0.07706592
To utilize spatial lattice process methods, SpatialQPFs provides
Areal_data() function. For details of the function, refer
to help(Areal_data)
ss_lattice_bi = Areal_data(path = path, file = file,
x_var = "x", y_var = "y", cell_class_var = "cell_id",
from_type = "Lym",
to_type = "TC",
scale = 200,
myplot = T)## Areal features are calculated.
## $BC
## [1] 0.5185003
##
## $MH_index
## [1] 0.3257691
##
## $JaccardJ
## [1] 0.2122787
##
## $SorensenL
## [1] 0.3502143
##
## $Moran_I_tumor
## [1] 0.03291831
##
## $Moran_I_immune
## [1] 0.6420521
##
## $moran_I_Bivariate
## [1] 0.06693746
##
## $geary_TC
## [1] 0.9751893
##
## $geary_IC
## [1] 0.5006019
##
## $moran_HL_TC
## [1] 0.003676471
##
## $moran_HH_TC
## [1] 0.02573529
##
## $moran_LH_TC
## [1] 0.009191176
##
## $moran_LL_TC
## [1] 0.003676471
##
## $geary_HH_TC
## [1] 0.02389706
##
## $geary_LL_TC
## [1] 0.003676471
##
## $geary_OP_TC
## [1] 0.009191176
##
## $geary_NE_TC
## [1] 0.007352941
##
## $moran_HL_IC
## [1] 0
##
## $moran_HH_IC
## [1] 0.07352941
##
## $moran_LH_IC
## [1] 0.007352941
##
## $moran_LL_IC
## [1] 0
##
## $geary_HH_IC
## [1] 0.06617647
##
## $geary_LL_IC
## [1] 0
##
## $geary_OP_IC
## [1] 0.003676471
##
## $geary_NE_IC
## [1] 0
##
## $moran_HH_TC_HH_IC
## [1] 0.001102941
##
## $moran_HH_TC_HL_IC
## [1] 0
##
## $moran_HH_TC_LH_IC
## [1] 0
##
## $moran_HH_TC_LL_IC
## [1] 0
##
## $moran_HL_TC_HH_IC
## [1] 0
##
## $moran_HL_TC_HL_IC
## [1] NA
##
## $moran_HL_TC_LH_IC
## [1] 0
##
## $moran_HL_TC_LL_IC
## [1] 0
##
## $moran_LH_TC_HH_IC
## [1] 0.0003676471
##
## $moran_LH_TC_HL_IC
## [1] 0
##
## $moran_LH_TC_LH_IC
## [1] 0
##
## $moran_LH_TC_LL_IC
## [1] 0
##
## $moran_LL_TC_HH_IC
## [1] 0
##
## $moran_LL_TC_HL_IC
## [1] NA
##
## $moran_LL_TC_LH_IC
## [1] 0
##
## $moran_LL_TC_LL_IC
## [1] 0
##
## $geary_HH_TC_HH_IC
## [1] 0.005514706
##
## $geary_HH_TC_LL_IC
## [1] 0
##
## $geary_HH_TC_OP_IC
## [1] 0
##
## $geary_HH_TC_NE_IC
## [1] 0
##
## $geary_LL_TC_HH_IC
## [1] 0
##
## $geary_LL_TC_LL_IC
## [1] 0
##
## $geary_LL_TC_OP_IC
## [1] 0
##
## $geary_LL_TC_NE_IC
## [1] NA
##
## $geary_OP_TC_HH_IC
## [1] 0
##
## $geary_OP_TC_LL_IC
## [1] 0
##
## $geary_OP_TC_OP_IC
## [1] 0
##
## $geary_OP_TC_NE_IC
## [1] 0
##
## $geary_NE_TC_HH_IC
## [1] 0
##
## $geary_NE_TC_LL_IC
## [1] 0
##
## $geary_NE_TC_OP_IC
## [1] 0
##
## $geary_NE_TC_NE_IC
## [1] NA
##
## $GetisOrd_HS
## [1] 0.009191176
##
## $GetisOrd_CS
## [1] 0
##
## $GetisOrd_CS_IC_HS_TC
## [1] 0
##
## $GetisOrd_CS_TC_HS_IC
## [1] 0
##
## $GetisOrd_HS_IC
## [1] 0.07536765
##
## $GetisOrd_CS_IC
## [1] 0.005514706
##
## $GetisOrd_HS_TC
## [1] 0.04044118
##
## $GetisOrd_CS_TC
## [1] 0.01286765
##
## $GetisOrd_S_intra_cancer
## [1] 0.9998912
##
## $GetisOrd_S_intra_immune
## [1] 0.9998912
##
## $Lee_L
## [1] 0.1589278
##
## $Lee_HL_TC_IC
## [1] 0.005514706
##
## $Lee_HH_TC_IC
## [1] 0.04779412
##
## $Lee_LH_TC_IC
## [1] 0.01838235
##
## $Lee_LL_TC_IC
## [1] 0.003676471
##
## $Lee_HL_IC_TC
## [1] 0.02205882
##
## $Lee_HH_IC_TC
## [1] 0.04227941
##
## $Lee_LH_IC_TC
## [1] 0.005514706
##
## $Lee_LL_IC_TC
## [1] 0.005514706
To utilize geostatistics process methods, SpatialQPFs provides
Geostatistics_data() function. For details of the function,
refer to help(Geostatistics_data)
ss_geostat_bi = Geostatistics_data(path = path, file = file,
x_var = "x", y_var = "y", cell_class_var = "cell_id",
from_type = "Lym",
to_type = "TC",
scale = 500,
myplot = T)## Geostatistics features are calculated.
## $sill_tumor
## [1] 1.22331e-05
##
## $sill_immune
## [1] 0.0003285947
##
## $range_tumor
## [1] 0.03298128
##
## $range_immune
## [1] 0.03490245
##
## $kappa_tumor
## [1] 1
##
## $kappa_immune
## [1] 5
##
## $sill_IC_TC
## [1] 0.0003022043
##
## $range_IC_TC
## [1] 0.03243775
##
## $kappa_IC_TC
## [1] 5
##
## $sill_TC_IC
## [1] 1.153167e-05
##
## $range_TC_IC
## [1] 0.01435864
##
## $kappa_TC_IC
## [1] 4.2
##
## $kappa_IK
## [1] 1
##
## $sill_IK
## [1] 0.2637792
##
## $range_IK
## [1] 0.1928726
graph_uni_TC = DT_graph_uni_subregion_random(path = path, file = file,
x_var = "x", y_var = "y", cell_class_var = "cell_id",
cell_type = "TC",
scale = 1000, side_length = 2000, num_FOV = 20, set_seed = 42, myplot = T)## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15
## [1] 16
## [1] 17
## [1] 18
## [1] 19
## [1] 20
graph_uni_Lym = DT_graph_uni_subregion_random(path = path, file = file,
x_var = "x", y_var = "y", cell_class_var = "cell_id",
cell_type = "Lym",
scale = 1000, side_length = 2000, num_FOV = 20, set_seed = 42, myplot = T)## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
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graph_all = DT_graph_cross_subregion_random(path = path, file = file,
x_var = "x", y_var = "y", cell_class_var = "cell_id",
cell_type = c("TC", "Lym", "Stroma", "Others"),
scale = 1000, side_length = 2000, num_FOV = 20, set_seed = 42, myplot = T)## Processing FOV: 1
## Processing FOV: 2
## Processing FOV: 3
## Processing FOV: 4
## Processing FOV: 5
## Processing FOV: 6
## Processing FOV: 7
## Processing FOV: 8
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## Processing FOV: 14
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## Processing FOV: 16
## Processing FOV: 17
## Processing FOV: 18
## Processing FOV: 19
## Processing FOV: 20
spat_entropy_all = spat_entropy_alltype(path = path, file = file,
cell_class_var = "cell_id",
x_var = "x", y_var = "y", scale = 200,
side_length = 2000, num_FOV = 10, set_seed = 42, myplot = T)## [1] 1
## Computing the pairwise distances for all observations. This may take some time...
## All done
## Computing the pairwise distances for all observations. This may take some time...
## Done
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
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## Computing the pairwise distances for all observations. This may take some time...
## All done
## Computing the pairwise distances for all observations. This may take some time...
## Done
## $shannon
## [1] 1.198687
##
## $shannonZ_entropy
## [1] 1.93634
##
## $altieri_entropy_SMI
## [1] 0.06840236
##
## $altieri_entropy_RES
## [1] 1.85931
##
## $leibovici_entropy
## [1] 2.27181
##
## $spat_diversity
## [1] 1.649454
spat_entropy_TC = spat_entropy_onetype(path = path, file = file,
cell_class_var = "cell_id",
x_var = "x", y_var = "y",
cell_type = "TC",
scale = 200, myplot = T)## $batty_entropy
## [1] -0.3319566
##
## $battyLISA_entropy
## [1] 6.262947
spat_entropy_Lym = spat_entropy_onetype(path = path, file = file,
cell_class_var = "cell_id",
x_var = "x", y_var = "y",
cell_type = "Lym",
scale = 200, myplot = T)## $batty_entropy
## [1] -1.285927
##
## $battyLISA_entropy
## [1] 5.580511
this_res = data.frame(cbind(data.frame("file" = file),
data.frame(ss_point_uni_TC) %>% setnames(paste0(names(.),"_TC")),
data.frame(ss_point_uni_Lym) %>% setnames(paste0(names(.),"_Lym")),
data.frame(ss_point_bi),
data.frame(ss_point_ITLR),
data.frame(ss_lattice_bi),
data.frame(ss_geostat_bi),
data.frame(graph_uni_TC) %>% setnames(paste0(names(.),"_TC")),
data.frame(graph_uni_Lym) %>% setnames(paste0(names(.),"_Lym")),
data.frame(graph_all),
data.frame(spat_entropy_all),
data.frame(spat_entropy_TC) %>% setnames(paste0(names(.),"_TC")),
data.frame(spat_entropy_Lym) %>% setnames(paste0(names(.),"_Lym")),
distribution_summary(spat_local_entropy_TC[!is.na(spat_local_entropy_TC)]) %>% setnames(paste0(names(.),"_le_TC")),
distribution_summary(spat_local_entropy_Lym[!is.na(spat_local_entropy_Lym)]) %>% setnames(paste0(names(.),"_le_Lym"))
),
row.names = NULL)
print(this_res)## file g_AUC_TC g_r_TC k_AUC_TC k_vals_med_TC k_vals_q1_TC
## 1 dat.csv -3.399809e-05 0.04474579 7.373894e-06 0.002108313 0.0005112325
## k_vals_q3_TC k_vals_max_TC pcf_AUC_TC pcf_vals_med_TC pcf_vals_q1_TC
## 1 0.004734315 0.008146708 0.002099142 1.067271 0.9994965
## pcf_vals_q3_TC pcf_vals_max_TC pcf_vals_min_TC pcf_r_TC CE_TC
## 1 1.08681 1.106596 0.7911587 0.01052842 1.008257
## g_AUC_Lym g_r_Lym k_AUC_Lym k_vals_med_Lym k_vals_q1_Lym
## 1 -0.002346612 0.05021089 -1.958382e-05 0.001847102 0.0003459852
## k_vals_q3_Lym k_vals_max_Lym pcf_AUC_Lym pcf_vals_med_Lym pcf_vals_q1_Lym
## 1 0.003609473 0.007333462 -0.005840654 0.8691311 0.7951248
## pcf_vals_q3_Lym pcf_vals_max_Lym pcf_vals_min_Lym pcf_r_Lym CE_Lym
## 1 0.9635727 1.104238 0.6671293 0.04492553 1.130428
## g_cross_AUC g_cross_r k_cross_AUC k_cross_vals_med k_cross_vals_q1
## 1 0.0006782376 0.03889667 2.426612e-05 0.002336459 0.0005660821
## k_cross_vals_q3 k_cross_vals_max K12_Diff_AUC k_k1k2_vals_med k_k1k2_vals_q1
## 1 0.005277495 0.00944047 0.0003790916 0.00594408 0.001634323
## k_k1k2_vals_q3 k_k1k2_vals_max K1K12_Diff_AUC k_k1k12_vals_med
## 1 0.01270813 0.02151166 0.0003633184 0.005750551
## k_k1k12_vals_q1 k_k1k12_vals_q3 k_k1k12_vals_max K2K12_Diff_AUC
## 1 0.001602771 0.0122064 0.02026278 -1.577328e-05
## k_k2k12_vals_med k_k2k12_vals_q1 k_k2k12_vals_q3 k_k2k12_vals_max pcf_AUC
## 1 -0.0001935285 -0.0005017328 -3.155209e-05 0 0.008457301
## pcf_vals_med pcf_vals_q1 pcf_vals_q3 pcf_vals_max pcf_vals_min pcf_r
## 1 1.21454 1.168805 1.22244 1.229431 1.091761 0.04445334
## mrcf_AUC mrcf_vals_med mrcf_vals_q1 mrcf_vals_q3 mrcf_vals_max
## 1 0.01127915 1.228653 1.20611 1.274725 1.277776
## mrcf_vals_min mrcf_r mccf_AUC mccf_vals_med mccf_vals_q1
## 1 0.8980466 0.02778333 -0.0003683821 0.05376596 0.05269271
## mccf_vals_q3 mccf_vals_max mccf_vals_min mccf_r M_AUC M_vals_med
## 1 0.05565426 0.05800203 0.04952488 0.04445334 0.1063055 3.591923
## M_vals_q1 M_vals_q3 M_vals_max M_vals_min M_r ITLR ITLR2
## 1 3.280865 4.116922 4.611528 0.4818792 0.03334 0.1852679 0.07706592
## BC MH_index JaccardJ SorensenL Moran_I_tumor Moran_I_immune
## 1 0.5185003 0.3257691 0.2122787 0.3502143 0.03291831 0.6420521
## moran_I_Bivariate geary_TC geary_IC moran_HL_TC moran_HH_TC moran_LH_TC
## 1 0.06693746 0.9751893 0.5006019 0.003676471 0.02573529 0.009191176
## moran_LL_TC geary_HH_TC geary_LL_TC geary_OP_TC geary_NE_TC moran_HL_IC
## 1 0.003676471 0.02389706 0.003676471 0.009191176 0.007352941 0
## moran_HH_IC moran_LH_IC moran_LL_IC geary_HH_IC geary_LL_IC geary_OP_IC
## 1 0.07352941 0.007352941 0 0.06617647 0 0.003676471
## geary_NE_IC moran_HH_TC_HH_IC moran_HH_TC_HL_IC moran_HH_TC_LH_IC
## 1 0 0.001102941 0 0
## moran_HH_TC_LL_IC moran_HL_TC_HH_IC moran_HL_TC_HL_IC moran_HL_TC_LH_IC
## 1 0 0 NA 0
## moran_HL_TC_LL_IC moran_LH_TC_HH_IC moran_LH_TC_HL_IC moran_LH_TC_LH_IC
## 1 0 0.0003676471 0 0
## moran_LH_TC_LL_IC moran_LL_TC_HH_IC moran_LL_TC_HL_IC moran_LL_TC_LH_IC
## 1 0 0 NA 0
## moran_LL_TC_LL_IC geary_HH_TC_HH_IC geary_HH_TC_LL_IC geary_HH_TC_OP_IC
## 1 0 0.005514706 0 0
## geary_HH_TC_NE_IC geary_LL_TC_HH_IC geary_LL_TC_LL_IC geary_LL_TC_OP_IC
## 1 0 0 0 0
## geary_LL_TC_NE_IC geary_OP_TC_HH_IC geary_OP_TC_LL_IC geary_OP_TC_OP_IC
## 1 NA 0 0 0
## geary_OP_TC_NE_IC geary_NE_TC_HH_IC geary_NE_TC_LL_IC geary_NE_TC_OP_IC
## 1 0 0 0 0
## geary_NE_TC_NE_IC GetisOrd_HS GetisOrd_CS GetisOrd_CS_IC_HS_TC
## 1 NA 0.009191176 0 0
## GetisOrd_CS_TC_HS_IC GetisOrd_HS_IC GetisOrd_CS_IC GetisOrd_HS_TC
## 1 0 0.07536765 0.005514706 0.04044118
## GetisOrd_CS_TC GetisOrd_S_intra_cancer GetisOrd_S_intra_immune Lee_L
## 1 0.01286765 0.9998912 0.9998912 0.1589278
## Lee_HL_TC_IC Lee_HH_TC_IC Lee_LH_TC_IC Lee_LL_TC_IC Lee_HL_IC_TC Lee_HH_IC_TC
## 1 0.005514706 0.04779412 0.01838235 0.003676471 0.02205882 0.04227941
## Lee_LH_IC_TC Lee_LL_IC_TC sill_tumor sill_immune range_tumor range_immune
## 1 0.005514706 0.005514706 1.22331e-05 0.0003285947 0.03298128 0.03490245
## kappa_tumor kappa_immune sill_IC_TC range_IC_TC kappa_IC_TC sill_TC_IC
## 1 1 5 0.0003022043 0.03243775 5 1.153167e-05
## range_TC_IC kappa_TC_IC kappa_IK sill_IK range_IK mean_x_weight_TC
## 1 0.01435864 4.2 1 0.2637792 0.1928726 341.9047
## median_x_weight_TC sd_x_weight_TC iqr_x_weight_TC skewness_x_weight_TC
## 1 316.3526 189.5267 253.4453 0.7379734
## kurtosis_x_weight_TC min_x_weight_TC max_x_weight_TC range_x_weight_TC
## 1 3.078021 46.42725 941.0975 888.2847
## Q10_weight_TC Q20_weight_TC Q25_weight_TC Q30_weight_TC Q40_weight_TC
## 1 129.9211 180.4079 207.8196 232.3328 276.2156
## Q60_weight_TC Q70_weight_TC Q75_weight_TC Q80_weight_TC Q90_weight_TC
## 1 364.2922 423.0589 444.1035 485.7368 598.8658
## theil_index_weight_TC gini_coeff_weight_TC mean_x_closeness_TC
## 1 0.1876369 0.3007102 0.0009336501
## median_x_closeness_TC sd_x_closeness_TC iqr_x_closeness_TC
## 1 0.0009218071 0.0001553146 0.0002475451
## skewness_x_closeness_TC kurtosis_x_closeness_TC min_x_closeness_TC
## 1 0.1378892 2.074654 0.0006501667
## max_x_closeness_TC range_x_closeness_TC Q10_closeness_TC Q20_closeness_TC
## 1 0.001222484 0.0005822904 0.000736942 0.0007896602
## Q25_closeness_TC Q30_closeness_TC Q40_closeness_TC Q60_closeness_TC
## 1 0.0008037539 0.0008276245 0.0008790804 0.0009688396
## Q70_closeness_TC Q75_closeness_TC Q80_closeness_TC Q90_closeness_TC
## 1 0.001035236 0.001060593 0.001083414 0.001155939
## theil_index_closeness_TC gini_coeff_closeness_TC mean_x_betweenness_TC
## 1 0.1422839 0.0938724 0.06023753
## median_x_betweenness_TC sd_x_betweenness_TC iqr_x_betweenness_TC
## 1 0.04720288 0.05340788 0.07404862
## skewness_x_betweenness_TC kurtosis_x_betweenness_TC min_x_betweenness_TC
## 1 0.8201344 2.842056 0
## max_x_betweenness_TC range_x_betweenness_TC Q10_betweenness_TC
## 1 0.2013879 0.2013879 0.0009756387
## Q20_betweenness_TC Q25_betweenness_TC Q30_betweenness_TC Q40_betweenness_TC
## 1 0.01213235 0.01654156 0.02337255 0.03363845
## Q60_betweenness_TC Q70_betweenness_TC Q75_betweenness_TC Q80_betweenness_TC
## 1 0.06376951 0.07771271 0.09206894 0.1045411
## Q90_betweenness_TC theil_index_betweenness_TC gini_coeff_betweenness_TC
## 1 0.1379019 0.2927516 0.483103
## mean_x_degree_TC median_x_degree_TC sd_x_degree_TC iqr_x_degree_TC
## 1 0.1212121 0.1149577 0.03124014 0.04174963
## skewness_x_degree_TC kurtosis_x_degree_TC min_x_degree_TC max_x_degree_TC
## 1 0.2111385 3.022713 0.06122449 0.1905842
## range_x_degree_TC Q10_degree_TC Q20_degree_TC Q25_degree_TC Q30_degree_TC
## 1 0.1306689 0.08792271 0.09529212 0.1028497 0.1111111
## Q40_degree_TC Q60_degree_TC Q70_degree_TC Q75_degree_TC Q80_degree_TC
## 1 0.1149577 0.1237245 0.1379493 0.1395349 0.1395349
## Q90_degree_TC theil_index_degree_TC gini_coeff_degree_TC
## 1 0.1609408 0.09894185 0.1364111
## mean_x_triangle_area_TC median_x_triangle_area_TC sd_x_triangle_area_TC
## 1 38044.83 27469.38 36202.47
## iqr_x_triangle_area_TC skewness_x_triangle_area_TC
## 1 35823.25 1.48537
## kurtosis_x_triangle_area_TC min_x_triangle_area_TC max_x_triangle_area_TC
## 1 5.028701 1807.25 164302
## range_x_triangle_area_TC Q10_triangle_area_TC Q20_triangle_area_TC
## 1 162182.7 7112.375 10863.6
## Q25_triangle_area_TC Q30_triangle_area_TC Q40_triangle_area_TC
## 1 13345.37 15286 20370.55
## Q60_triangle_area_TC Q70_triangle_area_TC Q75_triangle_area_TC
## 1 35461.45 44900.05 51195.44
## Q80_triangle_area_TC Q90_triangle_area_TC theil_index_triangle_area_TC
## 1 59217.7 90076.45 0.3679089
## gini_coeff_triangle_area_TC mean_x_triangle_perimeter_TC
## 1 0.4452052 998.2516
## median_x_triangle_perimeter_TC sd_x_triangle_perimeter_TC
## 1 957.7757 398.0856
## iqr_x_triangle_perimeter_TC skewness_x_triangle_perimeter_TC
## 1 540.3606 0.4805242
## kurtosis_x_triangle_perimeter_TC min_x_triangle_perimeter_TC
## 1 2.584347 309.7428
## max_x_triangle_perimeter_TC range_x_triangle_perimeter_TC
## 1 2024.183 1741.031
## Q10_triangle_perimeter_TC Q20_triangle_perimeter_TC Q25_triangle_perimeter_TC
## 1 525.6501 656.8388 701.4646
## Q30_triangle_perimeter_TC Q40_triangle_perimeter_TC Q60_triangle_perimeter_TC
## 1 756.5406 855.9714 1051.601
## Q70_triangle_perimeter_TC Q75_triangle_perimeter_TC Q80_triangle_perimeter_TC
## 1 1162.411 1240.914 1341.49
## Q90_triangle_perimeter_TC theil_index_triangle_perimeter_TC
## 1 1643.694 0.1631412
## gini_coeff_triangle_perimeter_TC mean_x_weight_Lym median_x_weight_Lym
## 1 0.2219247 559.5647 572.86
## sd_x_weight_Lym iqr_x_weight_Lym skewness_x_weight_Lym kurtosis_x_weight_Lym
## 1 235.2004 339.0111 0.023169 1.915156
## min_x_weight_Lym max_x_weight_Lym range_x_weight_Lym Q10_weight_Lym
## 1 158.1078 972.42 818.1371 275.4948
## Q20_weight_Lym Q25_weight_Lym Q30_weight_Lym Q40_weight_Lym Q60_weight_Lym
## 1 341.8807 373.8321 442.7825 497.452 622.288
## Q70_weight_Lym Q75_weight_Lym Q80_weight_Lym Q90_weight_Lym
## 1 708.5934 737.991 769.6201 861.7075
## theil_index_weight_Lym gini_coeff_weight_Lym mean_x_closeness_Lym
## 1 0.1517951 0.2334459 0.001118417
## median_x_closeness_Lym sd_x_closeness_Lym iqr_x_closeness_Lym
## 1 0.001090776 0.0002439847 0.0003185355
## skewness_x_closeness_Lym kurtosis_x_closeness_Lym min_x_closeness_Lym
## 1 0.006142384 1.959259 0.0006956157
## max_x_closeness_Lym range_x_closeness_Lym Q10_closeness_Lym Q20_closeness_Lym
## 1 0.001442123 0.000704349 0.000793602 0.0009014683
## Q25_closeness_Lym Q30_closeness_Lym Q40_closeness_Lym Q60_closeness_Lym
## 1 0.0009211277 0.0009442703 0.001029038 0.001176152
## Q70_closeness_Lym Q75_closeness_Lym Q80_closeness_Lym Q90_closeness_Lym
## 1 0.001274133 0.001293406 0.001301062 0.001364034
## theil_index_closeness_Lym gini_coeff_closeness_Lym mean_x_betweenness_Lym
## 1 0.1523647 0.1182617 0.1013528
## median_x_betweenness_Lym sd_x_betweenness_Lym iqr_x_betweenness_Lym
## 1 0.04756856 0.1213526 0.1348485
## skewness_x_betweenness_Lym kurtosis_x_betweenness_Lym min_x_betweenness_Lym
## 1 0.971388 2.333333 0
## max_x_betweenness_Lym range_x_betweenness_Lym Q10_betweenness_Lym
## 1 0.3333333 0.3333333 0
## Q20_betweenness_Lym Q25_betweenness_Lym Q30_betweenness_Lym
## 1 0 0 0
## Q40_betweenness_Lym Q60_betweenness_Lym Q70_betweenness_Lym
## 1 0.01623377 0.08430769 0.1209091
## Q75_betweenness_Lym Q80_betweenness_Lym Q90_betweenness_Lym
## 1 0.1545455 0.1733333 0.2212121
## theil_index_betweenness_Lym gini_coeff_betweenness_Lym mean_x_degree_Lym
## 1 0.08945023 0.6253289 0.3528139
## median_x_degree_Lym sd_x_degree_Lym iqr_x_degree_Lym skewness_x_degree_Lym
## 1 0.3452381 0.1435012 0.1785714 0.07244707
## kurtosis_x_degree_Lym min_x_degree_Lym max_x_degree_Lym range_x_degree_Lym
## 1 2.104167 0.1666667 0.6041667 0.4201681
## Q10_degree_Lym Q20_degree_Lym Q25_degree_Lym Q30_degree_Lym Q40_degree_Lym
## 1 0.1787879 0.2535714 0.2613636 0.2792208 0.3090909
## Q60_degree_Lym Q70_degree_Lym Q75_degree_Lym Q80_degree_Lym Q90_degree_Lym
## 1 0.3909091 0.4279221 0.4545455 0.4545455 0.4916667
## theil_index_degree_Lym gini_coeff_degree_Lym mean_x_triangle_area_Lym
## 1 0.05922949 0.1839387 79444.65
## median_x_triangle_area_Lym sd_x_triangle_area_Lym iqr_x_triangle_area_Lym
## 1 58704 54331.6 52902.75
## skewness_x_triangle_area_Lym kurtosis_x_triangle_area_Lym
## 1 0.7979994 2.14107
## min_x_triangle_area_Lym max_x_triangle_area_Lym range_x_triangle_area_Lym
## 1 15640 207540.5 162110.5
## Q10_triangle_area_Lym Q20_triangle_area_Lym Q25_triangle_area_Lym
## 1 24372.8 36250 39309.5
## Q30_triangle_area_Lym Q40_triangle_area_Lym Q60_triangle_area_Lym
## 1 43956.9 52201.5 78532.5
## Q70_triangle_area_Lym Q75_triangle_area_Lym Q80_triangle_area_Lym
## 1 89436.4 99501.5 108968
## Q90_triangle_area_Lym theil_index_triangle_area_Lym
## 1 144252.3 0.2912646
## gini_coeff_triangle_area_Lym mean_x_triangle_perimeter_Lym
## 1 0.3554519 1550.505
## median_x_triangle_perimeter_Lym sd_x_triangle_perimeter_Lym
## 1 1600.362 454.2837
## iqr_x_triangle_perimeter_Lym skewness_x_triangle_perimeter_Lym
## 1 474.4855 0.053512
## kurtosis_x_triangle_perimeter_Lym min_x_triangle_perimeter_Lym
## 1 1.916976 1124.175
## max_x_triangle_perimeter_Lym range_x_triangle_perimeter_Lym
## 1 2296.585 1223.054
## Q10_triangle_perimeter_Lym Q20_triangle_perimeter_Lym
## 1 1154.362 1225.364
## Q25_triangle_perimeter_Lym Q30_triangle_perimeter_Lym
## 1 1315.593 1317.962
## Q40_triangle_perimeter_Lym Q60_triangle_perimeter_Lym
## 1 1417.131 1619.769
## Q70_triangle_perimeter_Lym Q75_triangle_perimeter_Lym
## 1 1768.688 1828.992
## Q80_triangle_perimeter_Lym Q90_triangle_perimeter_Lym
## 1 1887.92 2013.812
## theil_index_triangle_perimeter_Lym gini_coeff_triangle_perimeter_Lym
## 1 0.1687758 0.1391456
## num_homotypic_edges num_heterotypic_edges graph_assortativity mean_degree_TC
## 1 98.5 156.5 -0.005663561 5.638937
## mean_lcc_TC mean_degree_Lym mean_lcc_Lym mean_degree_Stroma mean_lcc_Stroma
## 1 0.4447567 5.531206 0.4565286 5.6325 0.4532165
## mean_degree_Others mean_lcc_Others avg_prop_TC_neighbor_for_TC
## 1 5.577381 0.4589363 0.4335639
## avg_prop_Lym_neighbor_for_TC avg_prop_Stroma_neighbor_for_TC
## 1 0.08363859 0.3122982
## avg_prop_Others_neighbor_for_TC avg_prop_TC_neighbor_for_Lym
## 1 0.09094651 0.4532738
## avg_prop_Lym_neighbor_for_Lym avg_prop_Stroma_neighbor_for_Lym
## 1 0.08273584 0.3525794
## avg_prop_Others_neighbor_for_Lym avg_prop_TC_neighbor_for_Stroma
## 1 0.08365286 0.4181597
## avg_prop_Lym_neighbor_for_Stroma avg_prop_Stroma_neighbor_for_Stroma
## 1 0.09413217 0.3464766
## avg_prop_Others_neighbor_for_Stroma avg_prop_TC_neighbor_for_Others
## 1 0.09051435 0.4356656
## avg_prop_Lym_neighbor_for_Others avg_prop_Stroma_neighbor_for_Others
## 1 0.08122294 0.418835
## avg_prop_Others_neighbor_for_Others infiltration_score_TC_Lym
## 1 0.06378968 8.333333
## infiltration_score_TC_Stroma infiltration_score_TC_Others
## 1 2.388715 11.5
## infiltration_score_Lym_TC infiltration_score_Lym_Stroma
## 1 0.4627783 0.4494949
## infiltration_score_Lym_Others infiltration_score_Stroma_TC
## 1 2 1.317852
## infiltration_score_Stroma_Lym infiltration_score_Stroma_Others
## 1 8.5 9
## infiltration_score_Others_TC infiltration_score_Others_Lym
## 1 0.4001536 1.5
## infiltration_score_Others_Stroma mean_x_cross_weight median_x_cross_weight
## 1 0.5388994 257.1352 231.4548
## sd_x_cross_weight iqr_x_cross_weight skewness_x_cross_weight
## 1 144.4707 158.072 1.429373
## kurtosis_x_cross_weight min_x_cross_weight max_x_cross_weight
## 1 5.891466 17.72005 889.8029
## range_x_cross_weight Q10_cross_weight Q20_cross_weight Q25_cross_weight
## 1 863.7312 87.11175 131.2741 150.1614
## Q30_cross_weight Q40_cross_weight Q60_cross_weight Q70_cross_weight
## 1 164.8282 190.1411 262.2 292.7087
## Q75_cross_weight Q80_cross_weight Q90_cross_weight theil_index_cross_weight
## 1 319.2488 344.8599 429.1093 0.1944182
## gini_coeff_cross_weight shannon shannonZ_entropy altieri_entropy_SMI
## 1 0.3218815 1.198687 1.93634 0.06840236
## altieri_entropy_RES leibovici_entropy spat_diversity batty_entropy_TC
## 1 1.85931 2.27181 1.649454 -0.3319566
## battyLISA_entropy_TC batty_entropy_Lym battyLISA_entropy_Lym mean_x_le_TC
## 1 6.262947 -1.285927 5.580511 0.8242515
## median_x_le_TC sd_x_le_TC iqr_x_le_TC skewness_x_le_TC kurtosis_x_le_TC
## 1 0.9182958 0.5946244 1.298795 -0.2039206 1.873641
## min_x_le_TC max_x_le_TC range_x_le_TC Q10_le_TC Q20_le_TC Q25_le_TC Q30_le_TC
## 1 0 2 2 0 0 0 0.6500224
## Q40_le_TC Q60_le_TC Q70_le_TC Q75_le_TC Q80_le_TC Q90_le_TC theil_index_le_TC
## 1 0.9182958 1 1 1.298795 1.459148 1.548795 0.0389716
## gini_coeff_le_TC mean_x_le_Lym median_x_le_Lym sd_x_le_Lym iqr_x_le_Lym
## 1 0.3996172 1.065975 1 0.5560298 0.6887219
## skewness_x_le_Lym kurtosis_x_le_Lym min_x_le_Lym max_x_le_Lym range_x_le_Lym
## 1 -0.5801127 2.534307 0 2 2
## Q10_le_Lym Q20_le_Lym Q25_le_Lym Q30_le_Lym Q40_le_Lym Q60_le_Lym Q70_le_Lym
## 1 0 0.7219281 0.8112781 0.9182958 0.9852281 1.351644 1.459148
## Q75_le_Lym Q80_le_Lym Q90_le_Lym theil_index_le_Lym gini_coeff_le_Lym
## 1 1.5 1.530493 1.685816 0.04743391 0.2897993